Master Apache Spark Hands On!

Master Apache Spark – Hands On!, available at $69.99, has an average rating of 4.5, with 33 lectures, based on 1135 reviews, and has 7605 subscribers.

You will learn about Utilize the most powerful big data batch and stream processing engine to solve big data problems Master the new Spark Java Datasets API to slice and dice big data in an efficient manner Build, deploy and run Spark jobs on the cloud and bench mark performance on various hardware configurations Optimize spark clusters to work on big data efficiently and understand performance tuning Transform structured and semi-structured data using Spark SQL, Dataframes and Datasets Implement popular Machine Learning algorithms in Spark such as Linear Regression, Logistic Regression, and K-Means Clustering This course is ideal for individuals who are Anyone who is a Java developer and wants to add this seriously marketable technology on their resume or Anyone who wants to get into the data science field or Anyone who is interested in into the world of big data or Anyone who wants to implement machine learning algorithms in spark It is particularly useful for Anyone who is a Java developer and wants to add this seriously marketable technology on their resume or Anyone who wants to get into the data science field or Anyone who is interested in into the world of big data or Anyone who wants to implement machine learning algorithms in spark.

Enroll now: Master Apache Spark – Hands On!

Summary

Title: Master Apache Spark – Hands On!

Price: $69.99

Average Rating: 4.5

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Utilize the most powerful big data batch and stream processing engine to solve big data problems

Master the new Spark Java Datasets API to slice and dice big data in an efficient manner

Build, deploy and run Spark jobs on the cloud and bench mark performance on various hardware configurations

Optimize spark clusters to work on big data efficiently and understand performance tuning

Transform structured and semi-structured data using Spark SQL, Dataframes and Datasets

Implement popular Machine Learning algorithms in Spark such as Linear Regression, Logistic Regression, and K-Means Clustering

Who Should Attend

Anyone who is a Java developer and wants to add this seriously marketable technology on their resume

Anyone who wants to get into the data science field

Anyone who is interested in into the world of big data

Anyone who wants to implement machine learning algorithms in spark

Target Audiences

Anyone who is a Java developer and wants to add this seriously marketable technology on their resume

Anyone who wants to get into the data science field

Anyone who is interested in into the world of big data

Anyone who wants to implement machine learning algorithms in spark

LAST UPDATED: November 2023

Apache Spark is the next generation batch and stream processing engine. It’s been proven to be almost 100 times faster than Hadoop and much much easier to develop distributed big data applications with. It’s demand has sky rocketed in recent years and having this technology on your resume is truly a game changer. Over 3000 companies are using Spark in production right now and the list is growing very quickly!  Some of the big names include: Oracle, Hortonworks, Cisco, Verizon, Visa, Microsoft, Amazon as well as most of the big world banks and financial institutions! 

In this course you’ll learn everything you need to know about using Apache Spark in your organization while using their latest and greatest Java Datasets API.  Below are some of the things you’ll learn:

How to develop Spark Java Applications using Spark SQL Dataframes

Understand how the Spark Standalone cluster works behind the scenes

How to use various transformations to slice and dice your data in Spark Java

How to marshall/unmarshall Java domain objects (pojos) while working with Spark Datasets

Master joins, filters, aggregations and ingest data of various sizes and file formats (txt, csv, Json etc.)

Analyze over 18 million real-world comments on Redditto find the most trending words used

Develop programs using Spark Streaming for streaming stock market index files

Stream network sockets and messages queued on a Kafka cluster

Learn how to develop the most popular machine learning algorithms using Spark MLlib

Covers the most popular algorithms: Linear Regression, Logistic Regression and K-Means Clustering

You’ll be developing over 15 practical Spark Java applications crunching through real world data and slicing and dicing it in various ways using several data transformation techniques. This course is especially important for people who would like to be hired as a java developer or data engineer because Spark is a hugely sought after skill. We’ll even go over how to setup a live cluster and configure Spark Jobs to run on the cloud. You’ll also learn about the practical implications of performance tuning and scaling out a cluster to work with big data so you’ll definitely be learning a ton in this course. This course has a 30 day money back guarantee. You will have access to all of the code used in this course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why Spark

Lecture 2: Spark High Level Components

Lecture 3: Creating a Spark Maven Project

Lecture 4: Dedicated TA Support

Lecture 5: Join our Online Community (Discord)

Lecture 6: Import Source Code into Eclipse

Lecture 7: First Spark Application

Lecture 8: Spark Standalone Cluster Architecture

Chapter 2: Spark Java Dataset API Basics

Lecture 1: Ingesting CSV and JSON Files

Lecture 2: How to reduce logging in the console

Lecture 3: Real World Dataframes Example

Lecture 4: Union Dataframes and Other Set Transformations

Lecture 5: Converting Between Datasets and Dataframes

Chapter 3: Diving Deeper with Datasets, Dataframes, Transformations and the DAG

Lecture 1: Map and Reduce Transformation Functions

Lecture 2: Using Datasets with User Defined POJOs

Lecture 3: Using Datasets with Unstructured Textual Data

Lecture 4: Joining Dataframes and Using Various Filter Transformations

Lecture 5: Aggregation Transformations + Join Assignment

Lecture 6: More on Transformations, Actions and the DAG

Chapter 4: Running Spark Jobs on the Cloud

Lecture 1: Using Spark to Analyze Reddit Comments

Lecture 2: Running the Reddit Spark Application on an EMR Cluster

Lecture 3: Instructions for Configuring a Spark Stand-alone Cluster

Chapter 5: Spark Streaming Applications

Lecture 1: Streaming Network Socket Example

Lecture 2: Stock Market Files Streaming Example

Lecture 3: Using Kafka with Spark Streaming

Chapter 6: Machine Learning with Spark MLlib

Lecture 1: Machine Learning Resources

Lecture 2: Overview of Linear Regression

Lecture 3: Spark Java Linear Regression Example

Lecture 4: Overview of Logistic Regression

Lecture 5: Spark Java Logistic Regression (Classification Algorithm)

Lecture 6: Overview of K-Means Clustering

Lecture 7: Spark Java K-Means Clustering Example

Chapter 7: Course Extras!

Lecture 1: Bonus Lecture

Instructors

Job Ready Programmer
Senior Software Engineers and Trainers

Rating Distribution

1 stars: 7 votes

2 stars: 9 votes

3 stars: 86 votes

4 stars: 441 votes

5 stars: 592 votes

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